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10/11/2026

Read It Back Ep. 16: Can AI safety survive outside the test?

Can AI safety survive outside the test? Three posts from Anthropic, Eric Topol, and Senator Patty Murray, read word for word, then explained.

Posts & sources:
• Anthropic (): https://x.com/AnthropicAI/status/2108680150556737819
Source: https://www.anthropic.com/research/investigating-unintended-model-actions
• Eric Topol (): https://x.com/EricTopol/status/2108326818638131516
Source: https://arxiv.org/abs/2603.08448
• Senator Patty Murray (): https://x.com/PattyMurray/status/2108955957967651090
Source: https://www.murray.senate.gov/in-olympia-murray-hears-from-state-leaders-on-wa-efforts-to-regulate-ai-murray-emphasizes-need-for-federal-action-without-preemption/

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FULL TRANSCRIPT

HOST: Read It Back… from Medical Boulevard. [curious] Can AI safety survive OUTSIDE the test? [short pause] Anthropic, Eric Topol, and Senator Patty Murray show why the safeguards matter.

HOST: [thoughtful] Start with the boundary between practice and real life. Anthropic, the company behind Claude, has reported AI taking unintended actions on real websites. For medicine, that raises a basic question: what can the system ACTUALLY do? On October ninth, twenty twenty-six, Anthropic posted: We’re beginning a process of publishing more frequent reports on model behavior, beyond what appears in our system cards and regular risk reports. Today’s report describes four types of behaviors we’ve identified during evaluations and internal use. In each, Claude acted on real websites or systems in ways we didn’t intend, sometimes by working around a restriction instead of stopping. All cases had minimal real-world impact. From an alignment and security perspective, we consider these behaviors significantly less severe than the cybersecurity incidents we reported in July and September. Read the full report: There are links in this post. You'll find them in the caption.

READ-BACK: [thoughtful] An AI agent can use software tools to take action. Here, the company's incident report describes practice tasks spilling onto real websites. In one example, a model submitted… an invented police tip. Anthropic says the submission was caught as spam and never reached investigators. [serious] The fine print: Anthropic is reporting on its own product and has a commercial stake. It describes the impact as minimal and says it has tightened access and monitoring. That is a response to observed failures, not proof that EVERY future action will be safe. [emphatic] So, the read-back: a successful task can STILL cross a boundary. The question is what prevents an unwanted action… before it happens.

HOST: [curious] Now put that question in a clinic. A medical chatbot has been tested with real patients, while physicians watched the conversations live. Eric Topol, a cardiologist at Scripps Research, highlighted the study on October eighth, twenty twenty-six. Topol posted: Real-world AI goes to the urgent care setting. A prospective feasibility assessment of patient-facing conversational AI held up very well according to patients and physicians for accuracy of diagnosis, among many other metrics. Just published The Lancet, Adam Rodman, Peter Brodeur, Alan Karthikesalingam, Google DeepMind There are links in this post. You'll find them in the caption.

READ-BACK: [thoughtful] Amy is Google's research chatbot for medical conversations. Patients described their symptoms before a primary care visit. The system gathered a history and discussed possible diagnoses

10/10/2026

Read It Back Ep. 15: Does easier AI mean better care?

Does easier AI mean better care? Three posts from Mark Cuban, Daniel Kraft, MD, and Jonathan H Chen MD PhD, read word for word, then explained.

Posts & sources:
• Mark Cuban (): https://x.com/mcuban/status/2108653055927267794
Source: https://hai.stanford.edu/ai-index/2025-ai-index-report
• Daniel Kraft, MD (): https://x.com/daniel_kraft/status/2108275775363522710
Source: https://www.nature.com/articles/s41591-026-04626-w
• Jonathan H Chen MD PhD (): https://x.com/jonc101x/status/2108697911768809629
Source: https://www.arise-ai.org/blog/your-doctor-may-already-be-using-ai-only-1-in-4-americans-are-comfortable-with-that

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FULL TRANSCRIPT

HOST: [warmly] This is Read It Back, from Medical Boulevard. Today's question: does easier AI mean better care? Mark Cuban asks about the cost, Daniel Kraft brings a medical model, and Jonathan Chen brings the patient perspective. [curious] Three posts… and a gap between what we can build and what people NEED. Let's get into it.

HOST: [curious] Start with what it costs to get an answer. If AI becomes easier to run, more medical uses may become possible. But what EXACTLY gets cheaper? Mark Cuban, investor and co-founder of Cost Plus Drugs, raised that question on October ninth, twenty twenty-six. Cuban posted: What will the impact of improved algorithms be on the cost of compute ?

READ-BACK: Compute means the processing work a computer does. An algorithm is the method it follows. Inference is the part where a trained AI model responds to a request. Cuban is asking whether better methods change the price of that work. There is historical evidence behind the question… Stanford's twenty twenty-five AI Index reported that the cost of getting a particular level of language-model performance fell SHARPLY between late twenty twenty-two and late twenty twenty-four. [thoughtful] The fine print: that's a comparison at a specified performance level, over a specified period. It isn't a forecast of EVERY AI bill, and it doesn't measure what a hospital saves. Cuban brings an investor's perspective and a commercial stake in healthcare through his drug company, which competes with pharmacy middlemen. [emphatic] So, the read-back: a cheaper answer opens a door. It doesn't tell us whether walking through that door actually HELPS a patient. Keep the cost question, then add a second one: what can this particular tool actually do?

HOST: [thoughtful] That brings the cost question into medicine. Daniel Kraft, a physician and founder of Next-Med Health, highlighted Med-Gemma, Google's family of medical AI models. The appeal is the ability to run and adapt a model LOCALLY. On October eighth, twenty twenty-six, Kraft posted: Med-Gemma: Medical AI that can run locally, and adapt to local health needs. Med-Gemma demonstrates advanced medical understanding and reasoning across images and text and multiple medical imaging domains. Really impressive and impactful work from Google Research and Google DeepMind. In Nature Medicine There are links in this post. You'll find them in the caption.

READ-BACK: Med-Gemma works with medical words and images. Think of a starting engine for developers: something they can adapt for a particular job, rather than a finished service for a clinic. The Nature Medicine paper tests tasks including medical questions, image interpretation, and report generation. Running locally means the model can work on local computing equipment

10/09/2026

A blood vessel gets 20% narrower. How much blood flow is lost?

Most people guess 20%. Poiseuille's law says 59%. Flow depends on the radius to the 4th power (Q = πΔP r⁴ / 8ηL): halve the radius and flow drops to 1/16. Widen the vessel by just 19% and flow doubles.

Want the full lecture on this, taught by one of our leading physicians? Vote in the comments: YES or NO.

Precision Academy: https://www.medicalblvd.com/academy?utm_source=fb&utm_medium=social&utm_campaign=pa-poiseuille-short&utm_content=mb-r4

Idealized model: steady laminar flow, same pressure and vessel length. Educational content, not medical advice.

Medical Boulevard Precision Academy is an ADA CERP Recognized Provider. ADA CERP is a service of the American Dental Association to assist dental professionals in identifying quality providers of continuing dental education. ADA CERP does not approve or endorse individual courses or instructors, nor does it imply acceptance of credit hours by boards of dentistry.

10/09/2026

Read It Back Ep. 14: Can faster AI mean faster care?

Can faster AI mean faster care? Three posts from James Zou, Adam Rodman, and Christina Farr, read word for word, then explained.

Posts & sources:
• James Zou (): https://x.com/james_y_zou/status/2108232989406871847
Source: https://arxiv.org/abs/2604.11035
• Adam Rodman (): https://x.com/AdamRodmanMD/status/2108504901131141323
Source: https://arxiv.org/abs/2603.08448
• Christina Farr (): https://x.com/chrissyfarr/status/2108226710760046796
Source:https://www.amnhealthcare.com/siteassets/amn-insights/physician/ps-2025-physician-appt-wait-times---wp-v6.pdf

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For education, not medical advice. Every post is quoted as published.

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FULL TRANSCRIPT

HOST: [warmly] This is Read It Back, from Medical Boulevard. Can faster AI mean faster care? Today, James Zou, Adam Rodman, and Christina Farr take us from the speed of an answer… to the wait for an appointment. [curious] What has to happen in between? Let's read it back.

HOST: [curious] First, the engine. James Zou, a Stanford biomedical data science professor, is sharing research on generating text more efficiently. For medicine, that raises a bigger question: what does a faster answer ACTUALLY buy us? On October eighth, twenty twenty-six, Zou posted: Turns out that some introspection can greatly enhance diffusion language models! Our I D L M will be presented as a Oral Spotlight top less than one percent of submissions at C O L M twenty twenty-six today. Great job by Chenfeng Shoo and the Together AI team!

READ-BACK: A language model produces text. Many do it one piece at a time. Diffusion language models work on several pieces together. The research Zou highlights adds a check on earlier pieces while generating new ones. The authors call that introspection. It means consistency with the model's OWN predictions… not a machine thinking about its conscience. Their experiments report gains in text-generation quality and efficiency. [serious] The fine print: this is computing research accepted at a language-modeling conference, with tests including math and coding. It ISN'T a clinical trial. Zou is a coauthor and belongs to Together AI's research team, so he has a stake in the work. [emphatic] So, the read-back: a better engine is worth having. Whether it delivers better CARE… needs a different test. That's where the next post takes us.

HOST: [thoughtful] Now put an AI conversation inside a clinic. Adam Rodman, a practicing internist at Beth Israel Deaconess Medical Center, helped study a chatbot that talked with patients before their appointments. On October ninth, twenty twenty-six, Rodman posted: Our clinical trial of Amy, a patient-facing AI system, was just published in The Lancet. I want to explain what I think this study ACTUALLY means. It's about a fundamental question: how do we know that clinical AI works? There are links in this post. You'll find them in the caption.

READ-BACK: Amy is Google's research chatbot for medical conversations. In this feasibility study, patients discussed their symptoms with it before seeing a clinician. The clinician received a transcript and summary. A separate physician watched the chat live, with rules for stopping it. No conversation triggered a safety stop. That is ENCOURAGING evidence about a supervised workflow, rather than just another exam score. [serious] The fine print: it was a small, single-center study… without a randomized control group. Emergency presentations, pregnancy, and mental-health complaints were excluded. Rodman coauthored the study with Google researchers

10/07/2026

Read It Back Ep. 13: How does AI progress reach patients?

How does AI progress reach patients? Three posts from Greg Brockman, Berci Meskó, MD, PhD, and Tony Leachon MD, read word for word, then explained.

Posts & sources:
• Greg Brockman (): https://x.com/gdb/status/2107640886482104524
Source: https://github.com/openai/math
• Berci Meskó, MD, PhD (): https://x.com/Berci/status/2107743536204398755
Source: https://www.lciad.co.uk/robotic-dentistry-and-minimal-preparation-veneers/
• Tony Leachon MD (): https://x.com/DrTonyLeachon/status/2107282936395595918
Source: https://www.dbm.gov.ph/index.php/management-2/4236-gov-t-steps-up-fight-against-high-medical-bills-p74-45-b-for-philhealth-in-2027-including-p19-b-for-better-benefits-2

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For education, not medical advice. Every post is quoted as published.

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FULL TRANSCRIPT

HOST: [warmly] This is Read It Back, from Medical Boulevard. How does AI progress reach patients? Greg Brockman, Bertsi Meshko, and Tony Leachon take us from scientific promise… to the dental chair… to the bill.

HOST: [thoughtful] OpenAI has released mathematical manuscripts from an internal model. For doctors, the question is what makes a research result RELIABLE. Greg Brockman, OpenAI's president and co-founder, responded on October sixth, twenty twenty-six. Brockman posted: towards acceleration of scientific discovery and improving quality of life for everyone

READ-BACK: AI is producing written arguments for mathematical results. OpenAI has published manuscripts and supporting material for others to examine. Brockman connects that work to faster discovery and better lives. That's an ambition… not a patient outcome. [serious] The fine print: OpenAI says the results are at different stages of verification, and some could contain problems. Brockman leads the company behind the model — and has a direct stake in its success. [emphatic] So, the read-back: generating a result starts the story. Checking it makes it USABLE. For medicine, the next step is naming the task technology should help with.

HOST: [curious] Now a specific job: preparing teeth. A robot follows a plan… the clinical decisions reach further. Bertsi Meshko, a physician and director of The Medical Futurist Institute, made that distinction on October seventh, twenty twenty-six. Meshko posted: This is a great example of why we should stop asking whether robots will replace dentists. This robot is designed for one very specific task: preparing teeth for veneers according to a pre-approved digital treatment plan. It can physically work inside the patient’s mouth and remove only the amount of tooth structure specified in that plan, with very high precision. The dentist still diagnoses, plans the treatment, decides whether veneers are appropriate, supervises the procedure, manages complications, and places the final veneers. Robots don’t replace professions. They automate tasks. It's currently being deployed at this U K clinic.

READ-BACK: Veneers cover the fronts of teeth. Preparing the surface is one treatment step. Lupin describes software that creates a plan, and a supervised robot that carries it out. The London clinic offering it still describes consultation, dental-health checks, and planning beforehand. [serious] The fine print: these descriptions come from the manufacturer and a clinic selling the treatment. The clinic limits the current application to the upper front eight teeth. A workflow description doesn't establish better outcomes for everyone. [emphatic] So, the read-back: a precise task is a better starting point than replacing a profession. Evidence, then a bounded job with clinical oversight. Useful care still needs a way to REACH people.

HOST: [thoughtful] That brings us to access, through a separate story about Philippine health financing. Tony Leachon, a physician and former president of the Philippine College of Physicians, raised the funding question on October fifth, twenty twenty-six. Leachon posted: Who pays the price for this Philippine pesos one hundred seventy billion shortfall? All of us. While Phil-Health needs at least Philippine pesos two hundred forty-four billion to provide benefits for all Filipinos, only Philippine pesos seventy-four point four five billion has been given to the state insurer. Increasing health taxes can help close the funding gap while working as a preventive health measure. Health taxes are those imposed on harmful products such as to***co, v**e, alcohol, and sweetened beverages. When Filipinos shoulder a substantial amount of healthcare costs, it is a sign that the health system needs improving. It not only needs sustainable financing, but it also needs to prevent people from getting sick in the first place. There are links in this post. You'll find them in the caption.

READ-BACK: Phil-Health is the Philippines' public health insurer. Leachon argues for more funding and higher health taxes. His linked report compares a proposed subsidy with projected claims for government-subsidized members. This is a policy argument… not an AI savings study. [serious] The fine print: the allocation is a proposal for next year. The subsidy is separate from the insurer's operating budget. The quoted number isn't ALL of Phil-Health's money. Patients, taxpayers, and the insurer all have a stake. [emphatic] So, the read-back: a promising machine doesn't settle the funding question. Our connection is simple: check the evidence, define the job, and account for access. People need a way to benefit.

HOST: [warmly] AI progress reaches patients when evidence, a defined clinical task, and funded access come TOGETHER. Follow Medical Boulevard for tomorrow's read-back. Every post is quoted as published, with links in the description. This show is for education, not medical advice.

HOST: [warmly] This automated news aggregator is powered by cor-text technologies, in cooperation with Medical Boulevard. [curious] Want one for your own field? Click the link in the description, and build an automated news aggregator for you.

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10/07/2026

We let an AI doctor call our AI receptionist about the $495/mo Presence plan. Real demo call — both voices are AI.

What $495 a month gets you at Medical Boulevard:
• 6 treatment-room hours every month
• A Beverly Hills clinic address for your practice
• Month to month, cancel with 30 days' notice

Call Jackie 24/7 to inquire and book: (888) 658-9025
Medical Boulevard · 9025 Wilshire Blvd, Beverly Hills, CA 90211

10/06/2026

Read It Back Ep. 12: What counts as better care?

What counts as better care? Three posts from Sensors MDPI, Dr Alexey Kulikov, and UN News, read word for word, then explained.

Posts & sources:
• Sensors MDPI (): https://x.com/Sensors_MDPI/status/2107082122553139685
Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC12157247/
• Dr Alexey Kulikov (): https://x.com/KulikovUNIATF/status/2107128272110620983
Source: https://academic.oup.com/jamiaopen/article/9/4/ooag101/8726639
• UN News (): https://x.com/UN_News_Centre/status/2107440987832262932
Source: https://news.un.org/en/story/2026/10/1168527

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For education, not medical advice. Every post is quoted as published.

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FULL TRANSCRIPT

HOST: [warmly] This is Read It Back, from Medical Boulevard. [curious] What counts as better care? Sensors, Alexey Kulikov, and U N News take us from smarter tools… to what patients ACTUALLY receive.

HOST: [curious] Start underneath the AI headline, with the connection carrying a patient's data. On October fifth, twenty twenty-six, the research journal Sensors shared an earlier engineering study about keeping that connection reliable as patients move. Sensors posted: Intelligent Health Monitoring in six G Networks: Machine Learning-Enhanced V L C based Medical Body Sensor Networks adaptive modulation AI channel modeling channel estimation M L visible light communication V L C There are links in this post. You'll find them in the caption.

READ-BACK: Visible light communication means sending data using light. This study models hospital rooms where sensors communicate as a patient moves. Machine learning helps adjust how the signal is sent and estimate how it travels. Think of making the delivery route more reliable… before asking what the package contains. The source is an engineering paper published in twenty twenty-five, using simulations, rather than a trial of patient care. [serious] The fine print: performance depends on the room and receiver position. The authors report a tradeoff between transmission efficiency and keeping errors low. Sensors is sharing research it published

10/06/2026

We asked one AI phone agent to call the other. 😂 This is a real, unedited recording: Jackie, Medical Boulevard's new AI receptionist, answering an AI caller's questions about our Beverly Hills clinical space.

Call or text Jackie 24/7: (888) 658-9025
Treatment rooms by the hour · memberships · CO2 laser days

Medical Boulevard, 9025 Wilshire Boulevard, Suite 202, Beverly Hills, CA 90211

Developed by Cortxt Technologies, a division of Medical Boulevard.
Both voices in this video are AI.

10/05/2026

Mostly restorative and hygiene? Here's why Ozempic face is still your business.

An audience question from our live webinar. Plastic and reconstructive surgeon Dr. Eva Adanna Williams: you don't have to treat it, but knowing the whole face lets you counsel and refer. From Ozempic Face: Facial Harmony Beyond the Smile.

Ozempic Face & Dentistry: a CE course with Dr. Eva Williams, taught for dentists.

Course details: https://www.medicalblvd.com/academy/dentists/ozempic-face-and-dentistry?utm_source=fb&utm_medium=social&utm_campaign=pa-ozempic-shorts&utm_content=mb-restorative-hygiene

For licensed dental professionals. Educational content, not medical advice. Scope of practice varies by state.

Medical Boulevard Precision Academy is an ADA CERP Recognized Provider. ADA CERP is a service of the American Dental Association to assist dental professionals in identifying quality providers of continuing dental education. ADA CERP does not approve or endorse individual courses or instructors, nor does it imply acceptance of credit hours by boards of dentistry. Medical Boulevard Precision Academy designates this activity for up to 2 continuing education credits.

10/05/2026

Read It Back Ep. 11: Who checks AI’s big promises?

Who checks AI’s big promises? Three posts from Gary Marcus, STAT, and Ethan Mollick, read word for word, then explained.

Posts & sources:
• Gary Marcus (): https://x.com/GaryMarcus/status/2106548795555336296
Source:https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
• STAT (): https://x.com/statnews/status/2107058607623700914
Source: https://www.statnews.com/2026/10/05/could-ai-win-nobel-prize-medicine/
• Ethan Mollick (): https://x.com/emollick/status/2106936117421601110
Source: https://www.who.int/publications/i/item/9789240084759

Follow Medical Boulevard for a new read-back every day.
Discuss it with us on X:
For education, not medical advice. Every post is quoted as published.

This automated news aggregator is powered by cortxt technologies, in cooperation with Medical Boulevard. Build an automated news aggregator for you: https://cortxt.com/?utm_source=readitback&utm_medium=video&utm_campaign=news-aggregator

FULL TRANSCRIPT

HOST: [warmly] This is Read It Back, from Medical Boulevard. Today's question: who checks AI's big promises? With posts from Gary Marcus, STAT, and Ethan Mollick. From economic forecasts to medical discoveries, we'll follow the gap between a convincing claim… and something a doctor can TRUST.

HOST: [curious] Start with the promise. Gary Marcus, an emeritus psychology professor at New York University, questions a leap from spending on AI to transforming the economy. For medicine, the same distinction matters: investment is one thing… demonstrated benefit another. On October third, twenty twenty-six, Marcus posted: P S A: one of the most common rhetorical techniques in tech is to start with something very plausible (increase in cap-ex to four trillion dollars) and go to something that has almost zero chance (doubling G D P in less than a decade), while pretending the two of our equal likelihood. don’t be fooled.

READ-BACK: [thoughtful] Cap-ex means money spent on things like buildings and equipment. Gross domestic product measures an economy's output. Marcus is challenging the jump between those two: buying more computing power does not, by itself, demonstrate the payoff someone predicts. [serious] The fine print: this is Marcus's opinion, including his judgment about the odds. He has also founded AI companies

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